Overview

Dataset statistics

Number of variables23
Number of observations142193
Missing cells284122
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.0 MiB
Average record size in memory184.0 B

Variable types

Categorical2
Numeric19
Boolean2

Warnings

date has a high cardinality: 3436 distinct values High cardinality
mintemp is highly correlated with temp9amHigh correlation
maxtemp is highly correlated with temp3pmHigh correlation
humidity3pm is highly correlated with humidityHigh correlation
pressure9am is highly correlated with pressure3pmHigh correlation
pressure3pm is highly correlated with pressure9amHigh correlation
temp9am is highly correlated with mintempHigh correlation
temp3pm is highly correlated with maxtempHigh correlation
humidity is highly correlated with humidity3pmHigh correlation
evaporation has 60843 (42.8%) missing values Missing
sunshine has 67816 (47.7%) missing values Missing
humidity9am has 1774 (1.2%) missing values Missing
humidity3pm has 3610 (2.5%) missing values Missing
pressure9am has 14014 (9.9%) missing values Missing
pressure3pm has 13981 (9.8%) missing values Missing
cloud9am has 53657 (37.7%) missing values Missing
cloud3pm has 57094 (40.2%) missing values Missing
temp3pm has 2726 (1.9%) missing values Missing
humidity has 3610 (2.5%) missing values Missing
precipitation9am has unique values Unique
rainfall has 90275 (63.5%) zeros Zeros
sunshine has 2308 (1.6%) zeros Zeros
cloud9am has 8587 (6.0%) zeros Zeros
cloud3pm has 4957 (3.5%) zeros Zeros
amountOfRain has 91077 (64.1%) zeros Zeros

Reproduction

Analysis started2021-05-07 18:51:40.205684
Analysis finished2021-05-07 18:53:18.610933
Duration1 minute and 38.41 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

date
Categorical

HIGH CARDINALITY

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2017-05-15
 
49
2017-04-16
 
49
2014-07-23
 
49
2017-05-18
 
49
2014-01-16
 
49
Other values (3431)
141948 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1421930
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.1%

Sample

1st row2008-12-01
2nd row2008-12-02
3rd row2008-12-03
4th row2008-12-04
5th row2008-12-05
ValueCountFrequency (%)
2017-05-1549
 
< 0.1%
2017-04-1649
 
< 0.1%
2014-07-2349
 
< 0.1%
2017-05-1849
 
< 0.1%
2014-01-1649
 
< 0.1%
2013-12-0849
 
< 0.1%
2014-02-0349
 
< 0.1%
2016-08-2949
 
< 0.1%
2016-08-1149
 
< 0.1%
2017-01-1949
 
< 0.1%
Other values (3426)141703
99.7%
2021-05-07T15:53:18.936138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-05-1549
 
< 0.1%
2017-04-1649
 
< 0.1%
2014-07-2349
 
< 0.1%
2017-05-1849
 
< 0.1%
2014-01-1649
 
< 0.1%
2013-12-0849
 
< 0.1%
2014-02-0349
 
< 0.1%
2016-08-2949
 
< 0.1%
2016-08-1149
 
< 0.1%
2017-01-1949
 
< 0.1%
Other values (3426)141703
99.7%

Most occurring characters

ValueCountFrequency (%)
0353160
24.8%
-284386
20.0%
1260408
18.3%
2238931
16.8%
350191
 
3.5%
544306
 
3.1%
643905
 
3.1%
442723
 
3.0%
941662
 
2.9%
734284
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1137544
80.0%
Dash Punctuation284386
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0353160
31.0%
1260408
22.9%
2238931
21.0%
350191
 
4.4%
544306
 
3.9%
643905
 
3.9%
442723
 
3.8%
941662
 
3.7%
734284
 
3.0%
827974
 
2.5%
ValueCountFrequency (%)
-284386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1421930
100.0%

Most frequent character per script

ValueCountFrequency (%)
0353160
24.8%
-284386
20.0%
1260408
18.3%
2238931
16.8%
350191
 
3.5%
544306
 
3.1%
643905
 
3.1%
442723
 
3.0%
941662
 
2.9%
734284
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1421930
100.0%

Most frequent character per block

ValueCountFrequency (%)
0353160
24.8%
-284386
20.0%
1260408
18.3%
2238931
16.8%
350191
 
3.5%
544306
 
3.1%
643905
 
3.1%
442723
 
3.0%
941662
 
2.9%
734284
 
2.4%

location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3418
Sydney
 
3337
Perth
 
3193
Darwin
 
3192
Hobart
 
3188
Other values (44)
125865 

Length

Max length16
Median length8
Mean length8.703206206
Min length4

Characters and Unicode

Total characters1237535
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury
ValueCountFrequency (%)
Canberra3418
 
2.4%
Sydney3337
 
2.3%
Perth3193
 
2.2%
Darwin3192
 
2.2%
Hobart3188
 
2.2%
Brisbane3161
 
2.2%
Adelaide3090
 
2.2%
Bendigo3034
 
2.1%
Townsville3033
 
2.1%
AliceSprings3031
 
2.1%
Other values (39)110516
77.7%
2021-05-07T15:53:19.340618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra3418
 
2.4%
sydney3337
 
2.3%
perth3193
 
2.2%
darwin3192
 
2.2%
hobart3188
 
2.2%
brisbane3161
 
2.2%
adelaide3090
 
2.2%
bendigo3034
 
2.1%
townsville3033
 
2.1%
alicesprings3031
 
2.1%
Other values (39)110516
77.7%

Most occurring characters

ValueCountFrequency (%)
a115725
 
9.4%
r114339
 
9.2%
o106342
 
8.6%
e101043
 
8.2%
n88369
 
7.1%
l76287
 
6.2%
i74395
 
6.0%
t58309
 
4.7%
d36311
 
2.9%
s35975
 
2.9%
Other values (30)430440
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1045618
84.5%
Uppercase Letter191917
 
15.5%

Most frequent character per category

ValueCountFrequency (%)
a115725
11.1%
r114339
10.9%
o106342
10.2%
e101043
9.7%
n88369
 
8.5%
l76287
 
7.3%
i74395
 
7.1%
t58309
 
5.6%
d36311
 
3.5%
s35975
 
3.4%
Other values (12)238523
22.8%
ValueCountFrequency (%)
A26695
13.9%
W23248
12.1%
C18255
9.5%
M17242
9.0%
S15328
8.0%
P14924
7.8%
N13419
7.0%
B12151
6.3%
G11872
 
6.2%
H9070
 
4.7%
Other values (8)29713
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1237535
100.0%

Most frequent character per script

ValueCountFrequency (%)
a115725
 
9.4%
r114339
 
9.2%
o106342
 
8.6%
e101043
 
8.2%
n88369
 
7.1%
l76287
 
6.2%
i74395
 
6.0%
t58309
 
4.7%
d36311
 
2.9%
s35975
 
2.9%
Other values (30)430440
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1237535
100.0%

Most frequent character per block

ValueCountFrequency (%)
a115725
 
9.4%
r114339
 
9.2%
o106342
 
8.6%
e101043
 
8.2%
n88369
 
7.1%
l76287
 
6.2%
i74395
 
6.0%
t58309
 
4.7%
d36311
 
2.9%
s35975
 
2.9%
Other values (30)430440
34.8%

mintemp
Real number (ℝ)

HIGH CORRELATION

Distinct389
Distinct (%)0.3%
Missing637
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean12.18639973
Minimum-8.5
Maximum33.9
Zeros156
Zeros (%)0.1%
Memory size1.1 MiB
2021-05-07T15:53:19.508450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.8
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.403282675
Coefficient of variation (CV)0.5254449893
Kurtosis-0.4872527487
Mean12.18639973
Median Absolute Deviation (MAD)4.6
Skewness0.02389982065
Sum1725058
Variance41.00202901
MonotocityNot monotonic
2021-05-07T15:53:19.665592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6883
 
0.6%
11883
 
0.6%
10.2880
 
0.6%
10.5867
 
0.6%
10.8860
 
0.6%
9853
 
0.6%
12850
 
0.6%
10849
 
0.6%
13844
 
0.6%
10.4842
 
0.6%
Other values (379)132945
93.5%
ValueCountFrequency (%)
-8.51
< 0.1%
-8.22
< 0.1%
-82
< 0.1%
-7.81
< 0.1%
-7.62
< 0.1%
ValueCountFrequency (%)
33.91
 
< 0.1%
31.91
 
< 0.1%
31.81
 
< 0.1%
31.43
< 0.1%
31.21
 
< 0.1%

maxtemp
Real number (ℝ)

HIGH CORRELATION

Distinct505
Distinct (%)0.4%
Missing322
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean23.22678419
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:19.836409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.117618141
Coefficient of variation (CV)0.306440103
Kurtosis-0.2384461504
Mean23.22678419
Median Absolute Deviation (MAD)5.1
Skewness0.2249166146
Sum3295207.1
Variance50.660488
MonotocityNot monotonic
2021-05-07T15:53:20.023643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20871
 
0.6%
19.8829
 
0.6%
19827
 
0.6%
20.4820
 
0.6%
20.8804
 
0.6%
19.9803
 
0.6%
19.5801
 
0.6%
21799
 
0.6%
18.5793
 
0.6%
18.2792
 
0.6%
Other values (495)133732
94.0%
ValueCountFrequency (%)
-4.81
< 0.1%
-4.11
< 0.1%
-3.81
< 0.1%
-3.71
< 0.1%
-3.21
< 0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.32
< 0.1%
471
 
< 0.1%
46.91
 
< 0.1%
46.83
< 0.1%

rainfall
Real number (ℝ≥0)

ZEROS

Distinct679
Distinct (%)0.5%
Missing1406
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.349974074
Minimum0
Maximum371
Zeros90275
Zeros (%)63.5%
Memory size1.1 MiB
2021-05-07T15:53:20.451529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.465172918
Coefficient of variation (CV)3.602240982
Kurtosis180.0020968
Mean2.349974074
Median Absolute Deviation (MAD)0
Skewness9.888061068
Sum330845.8
Variance71.65915253
MonotocityNot monotonic
2021-05-07T15:53:20.607084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
090275
63.5%
0.28685
 
6.1%
0.43750
 
2.6%
0.62562
 
1.8%
0.82028
 
1.4%
11747
 
1.2%
1.21515
 
1.1%
1.41365
 
1.0%
1.61187
 
0.8%
1.81088
 
0.8%
Other values (669)26585
 
18.7%
(Missing)1406
 
1.0%
ValueCountFrequency (%)
090275
63.5%
0.1154
 
0.1%
0.28685
 
6.1%
0.364
 
< 0.1%
0.43750
 
2.6%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%

evaporation
Real number (ℝ≥0)

MISSING

Distinct356
Distinct (%)0.4%
Missing60843
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean5.469824216
Minimum0
Maximum145
Zeros240
Zeros (%)0.2%
Memory size1.1 MiB
2021-05-07T15:53:20.793585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.188536509
Coefficient of variation (CV)0.7657534033
Kurtosis45.06778373
Mean5.469824216
Median Absolute Deviation (MAD)2.4
Skewness3.746833979
Sum444970.2
Variance17.54383809
MonotocityNot monotonic
2021-05-07T15:53:20.952161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43282
 
2.3%
82574
 
1.8%
2.22057
 
1.4%
21996
 
1.4%
2.61975
 
1.4%
2.41963
 
1.4%
1.81945
 
1.4%
31937
 
1.4%
3.41934
 
1.4%
3.21918
 
1.3%
Other values (346)59769
42.0%
(Missing)60843
42.8%
ValueCountFrequency (%)
0240
 
0.2%
0.18
 
< 0.1%
0.2497
0.3%
0.310
 
< 0.1%
0.4760
0.5%
ValueCountFrequency (%)
1451
< 0.1%
86.21
< 0.1%
82.41
< 0.1%
81.21
< 0.1%
77.31
< 0.1%

sunshine
Real number (ℝ≥0)

MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing67816
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean7.624853113
Minimum0
Maximum14.5
Zeros2308
Zeros (%)1.6%
Memory size1.1 MiB
2021-05-07T15:53:21.132679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.9
median8.5
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.781524994
Coefficient of variation (CV)0.495947258
Kurtosis-0.8203636955
Mean7.624853113
Median Absolute Deviation (MAD)2.6
Skewness-0.5029112767
Sum567113.7
Variance14.29993128
MonotocityNot monotonic
2021-05-07T15:53:21.293119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02308
 
1.6%
10.71087
 
0.8%
111078
 
0.8%
10.81058
 
0.7%
10.51018
 
0.7%
10.91013
 
0.7%
10.3999
 
0.7%
10.2985
 
0.7%
10973
 
0.7%
11.1967
 
0.7%
Other values (135)62891
44.2%
(Missing)67816
47.7%
ValueCountFrequency (%)
02308
1.6%
0.1533
 
0.4%
0.2511
 
0.4%
0.3422
 
0.3%
0.4319
 
0.2%
ValueCountFrequency (%)
14.51
 
< 0.1%
14.34
 
< 0.1%
14.22
 
< 0.1%
14.16
 
< 0.1%
1415
< 0.1%

humidity9am
Real number (ℝ≥0)

MISSING

Distinct101
Distinct (%)0.1%
Missing1774
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean68.84381031
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:21.480579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.05129254
Coefficient of variation (CV)0.2767321049
Kurtosis-0.03924572083
Mean68.84381031
Median Absolute Deviation (MAD)13
Skewness-0.4828207735
Sum9666979
Variance362.9517473
MonotocityNot monotonic
2021-05-07T15:53:21.652920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
993350
 
2.4%
702985
 
2.1%
692962
 
2.1%
682961
 
2.1%
652952
 
2.1%
712939
 
2.1%
662916
 
2.1%
672895
 
2.0%
642867
 
2.0%
752859
 
2.0%
Other values (91)110733
77.9%
ValueCountFrequency (%)
01
 
< 0.1%
15
 
< 0.1%
28
 
< 0.1%
310
< 0.1%
420
< 0.1%
ValueCountFrequency (%)
1002827
2.0%
993350
2.4%
982063
1.5%
971757
1.2%
961577
1.1%

humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing3610
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.48260609
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:21.838634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.79777184
Coefficient of variation (CV)0.4039766714
Kurtosis-0.511101194
Mean51.48260609
Median Absolute Deviation (MAD)14
Skewness0.03451544293
Sum7134614
Variance432.5473137
MonotocityNot monotonic
2021-05-07T15:53:21.999708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
522699
 
1.9%
552685
 
1.9%
572679
 
1.9%
532650
 
1.9%
592639
 
1.9%
582604
 
1.8%
542595
 
1.8%
512577
 
1.8%
562574
 
1.8%
502573
 
1.8%
Other values (91)112308
79.0%
(Missing)3610
 
2.5%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
363
< 0.1%
4113
0.1%
ValueCountFrequency (%)
100393
0.3%
99428
0.3%
98588
0.4%
97393
0.3%
96451
0.3%

pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing14014
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1017.653758
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-07T15:53:22.167289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.105475712
Coefficient of variation (CV)0.006982213403
Kurtosis0.2361999825
Mean1017.653758
Median Absolute Deviation (MAD)4.7
Skewness-0.09621089388
Sum130441841.1
Variance50.48778509
MonotocityNot monotonic
2021-05-07T15:53:22.315864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4804
 
0.6%
1017.9779
 
0.5%
1018.7764
 
0.5%
1018761
 
0.5%
1015.9757
 
0.5%
1017.3756
 
0.5%
1017.8755
 
0.5%
1016.3753
 
0.5%
1017.2745
 
0.5%
1017.7744
 
0.5%
Other values (536)120561
84.8%
(Missing)14014
 
9.9%
ValueCountFrequency (%)
980.51
< 0.1%
9821
< 0.1%
982.21
< 0.1%
982.31
< 0.1%
982.92
< 0.1%
ValueCountFrequency (%)
10411
 
< 0.1%
1040.91
 
< 0.1%
1040.62
< 0.1%
1040.51
 
< 0.1%
1040.43
< 0.1%

pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing13981
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean1015.258204
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-07T15:53:22.489399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.036676783
Coefficient of variation (CV)0.006930923344
Kurtosis0.1325207865
Mean1015.258204
Median Absolute Deviation (MAD)4.8
Skewness-0.04619761861
Sum130168284.8
Variance49.51482016
MonotocityNot monotonic
2021-05-07T15:53:22.642020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.5773
 
0.5%
1015.3767
 
0.5%
1015.7763
 
0.5%
1015.6761
 
0.5%
1015.1752
 
0.5%
1013.5751
 
0.5%
1015.8751
 
0.5%
1015.4745
 
0.5%
1016738
 
0.5%
1014.8735
 
0.5%
Other values (539)120676
84.9%
(Missing)13981
 
9.8%
ValueCountFrequency (%)
977.11
< 0.1%
978.21
< 0.1%
9791
< 0.1%
980.22
< 0.1%
981.21
< 0.1%
ValueCountFrequency (%)
1039.61
< 0.1%
1038.91
< 0.1%
1038.51
< 0.1%
1038.41
< 0.1%
1038.21
< 0.1%

cloud9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing53657
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean4.437189392
Minimum0
Maximum9
Zeros8587
Zeros (%)6.0%
Memory size1.1 MiB
2021-05-07T15:53:22.804093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.887015526
Coefficient of variation (CV)0.6506405904
Kurtosis-1.5411594
Mean4.437189392
Median Absolute Deviation (MAD)3
Skewness-0.2242855389
Sum392851
Variance8.334858646
MonotocityNot monotonic
2021-05-07T15:53:22.934014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
719749
 
13.9%
115558
 
10.9%
814389
 
10.1%
08587
 
6.0%
68072
 
5.7%
26442
 
4.5%
35854
 
4.1%
55510
 
3.9%
44373
 
3.1%
92
 
< 0.1%
(Missing)53657
37.7%
ValueCountFrequency (%)
08587
6.0%
115558
10.9%
26442
4.5%
35854
 
4.1%
44373
 
3.1%
ValueCountFrequency (%)
92
 
< 0.1%
814389
10.1%
719749
13.9%
68072
5.7%
55510
 
3.9%

cloud3pm
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing57094
Missing (%)40.2%
Infinite0
Infinite (%)0.0%
Mean4.5031669
Minimum0
Maximum9
Zeros4957
Zeros (%)3.5%
Memory size1.1 MiB
2021-05-07T15:53:23.084123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.72063253
Coefficient of variation (CV)0.604159826
Kurtosis-1.457932583
Mean4.5031669
Median Absolute Deviation (MAD)2
Skewness-0.2240923649
Sum383215
Variance7.401841365
MonotocityNot monotonic
2021-05-07T15:53:23.199814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
718052
 
12.7%
114827
 
10.4%
812407
 
8.7%
68869
 
6.2%
27153
 
5.0%
36836
 
4.8%
56743
 
4.7%
45254
 
3.7%
04957
 
3.5%
91
 
< 0.1%
(Missing)57094
40.2%
ValueCountFrequency (%)
04957
 
3.5%
114827
10.4%
27153
5.0%
36836
4.8%
45254
 
3.7%
ValueCountFrequency (%)
91
 
< 0.1%
812407
8.7%
718052
12.7%
68869
6.2%
56743
 
4.7%

temp9am
Real number (ℝ)

HIGH CORRELATION

Distinct440
Distinct (%)0.3%
Missing904
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.98750858
Minimum-7.2
Maximum40.2
Zeros35
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:23.356395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.492838325
Coefficient of variation (CV)0.3822125119
Kurtosis-0.3491547666
Mean16.98750858
Median Absolute Deviation (MAD)4.6
Skewness0.09138682047
Sum2400148.1
Variance42.15694952
MonotocityNot monotonic
2021-05-07T15:53:23.520792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17901
 
0.6%
13.8887
 
0.6%
14.8873
 
0.6%
16869
 
0.6%
14855
 
0.6%
16.6855
 
0.6%
15852
 
0.6%
16.5844
 
0.6%
13831
 
0.6%
15.4827
 
0.6%
Other values (430)132695
93.3%
(Missing)904
 
0.6%
ValueCountFrequency (%)
-7.21
< 0.1%
-71
< 0.1%
-6.21
< 0.1%
-5.91
< 0.1%
-5.62
< 0.1%
ValueCountFrequency (%)
40.21
< 0.1%
39.41
< 0.1%
39.11
< 0.1%
391
< 0.1%
38.91
< 0.1%

temp3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct500
Distinct (%)0.4%
Missing2726
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean21.68723497
Minimum-5.4
Maximum46.7
Zeros16
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:23.693332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.937593869
Coefficient of variation (CV)0.3198929636
Kurtosis-0.1464607024
Mean21.68723497
Median Absolute Deviation (MAD)4.9
Skewness0.2400541927
Sum3024653.6
Variance48.13020868
MonotocityNot monotonic
2021-05-07T15:53:23.848916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20871
 
0.6%
19858
 
0.6%
18.5856
 
0.6%
18.4856
 
0.6%
17.8845
 
0.6%
19.2826
 
0.6%
19.4825
 
0.6%
18821
 
0.6%
19.3821
 
0.6%
17814
 
0.6%
Other values (490)131074
92.2%
(Missing)2726
 
1.9%
ValueCountFrequency (%)
-5.41
< 0.1%
-5.11
< 0.1%
-4.41
< 0.1%
-4.21
< 0.1%
-4.11
< 0.1%
ValueCountFrequency (%)
46.71
 
< 0.1%
46.21
 
< 0.1%
46.13
< 0.1%
45.91
 
< 0.1%
45.82
< 0.1%

raintoday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1406
Missing (%)1.0%
Memory size277.8 KiB
False
109332 
True
31455 
(Missing)
 
1406
ValueCountFrequency (%)
False109332
76.9%
True31455
 
22.1%
(Missing)1406
 
1.0%
2021-05-07T15:53:23.969565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

amountOfRain
Real number (ℝ≥0)

ZEROS

Distinct681
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.360681609
Minimum0
Maximum371
Zeros91077
Zeros (%)64.1%
Memory size1.1 MiB
2021-05-07T15:53:24.096226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.477969069
Coefficient of variation (CV)3.591322539
Kurtosis178.1682517
Mean2.360681609
Median Absolute Deviation (MAD)0
Skewness9.836902495
Sum335672.4
Variance71.87595954
MonotocityNot monotonic
2021-05-07T15:53:24.251810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
091077
64.1%
0.28762
 
6.2%
0.43781
 
2.7%
0.62591
 
1.8%
0.82055
 
1.4%
11761
 
1.2%
1.21535
 
1.1%
1.41379
 
1.0%
1.61201
 
0.8%
1.81104
 
0.8%
Other values (671)26947
 
19.0%
ValueCountFrequency (%)
091077
64.1%
0.1157
 
0.1%
0.28762
 
6.2%
0.365
 
< 0.1%
0.43781
 
2.7%
ValueCountFrequency (%)
3711
< 0.1%
367.61
< 0.1%
278.41
< 0.1%
268.61
< 0.1%
247.21
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.0 KiB
False
110316 
True
31877 
ValueCountFrequency (%)
False110316
77.6%
True31877
 
22.4%
2021-05-07T15:53:24.394934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

temp
Real number (ℝ)

Distinct7620
Distinct (%)5.4%
Missing322
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean28.50541942
Minimum-3.76
Maximum59.72
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-07T15:53:24.535067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3.76
5-th percentile3.425738507
Q122.52
median28.52
Q335.48
95-th percentile44.36
Maximum59.72
Range63.48
Interquartile range (IQR)12.96

Descriptive statistics

Standard deviation10.23750611
Coefficient of variation (CV)0.359142448
Kurtosis0.4742258563
Mean28.50541942
Median Absolute Deviation (MAD)6.36
Skewness-0.4242488265
Sum4044092.358
Variance104.8065314
MonotocityNot monotonic
2021-05-07T15:53:24.694669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26830
 
0.6%
25.76796
 
0.6%
24.8787
 
0.6%
26.48771
 
0.5%
25.88766
 
0.5%
25.4759
 
0.5%
26.96756
 
0.5%
24.68756
 
0.5%
27.2755
 
0.5%
23.84752
 
0.5%
Other values (7610)134143
94.3%
ValueCountFrequency (%)
-3.761
< 0.1%
-2.921
< 0.1%
-2.561
< 0.1%
-1.841
< 0.1%
-1.722
< 0.1%
ValueCountFrequency (%)
59.721
 
< 0.1%
58.762
< 0.1%
58.41
 
< 0.1%
58.281
 
< 0.1%
58.163
< 0.1%

humidity
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4163
Distinct (%)3.0%
Missing3610
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean61.99117945
Minimum2
Maximum122
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-07T15:53:24.883600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15.2
Q144
median63.2
Q380
95-th percentile106.4
Maximum122
Range120
Interquartile range (IQR)36

Descriptive statistics

Standard deviation26.64911127
Coefficient of variation (CV)0.4298855339
Kurtosis-0.397086155
Mean61.99117945
Median Absolute Deviation (MAD)18
Skewness-0.1227574279
Sum8590923.622
Variance710.1751317
MonotocityNot monotonic
2021-05-07T15:53:25.060632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.42621
 
1.8%
682609
 
1.8%
70.42603
 
1.8%
65.62570
 
1.8%
72.82556
 
1.8%
71.62526
 
1.8%
66.82513
 
1.8%
63.22494
 
1.8%
69.22487
 
1.7%
622482
 
1.7%
Other values (4153)113122
79.6%
(Missing)3610
 
2.5%
ValueCountFrequency (%)
24
< 0.1%
2.000285661
 
< 0.1%
2.0005134141
 
< 0.1%
2.0010653111
 
< 0.1%
2.0011859811
 
< 0.1%
ValueCountFrequency (%)
122380
0.3%
120.8417
0.3%
119.6569
0.4%
118.4385
0.3%
117.2432
0.3%

precipitation3pm
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01416385
Minimum0
Maximum26
Zeros14
Zeros (%)< 0.1%
Memory size1.1 MiB
2021-05-07T15:53:25.225698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q312
95-th percentile15
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.169831704
Coefficient of variation (CV)0.3165348352
Kurtosis0.09181025553
Mean10.01416385
Median Absolute Deviation (MAD)2
Skewness0.3130080553
Sum1423944
Variance10.04783303
MonotocityNot monotonic
2021-05-07T15:53:25.389260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
917746
12.5%
1017617
12.4%
1116233
11.4%
815970
11.2%
1213490
9.5%
712800
9.0%
1310419
7.3%
68999
6.3%
147422
5.2%
55271
 
3.7%
Other values (17)16226
11.4%
ValueCountFrequency (%)
014
 
< 0.1%
157
 
< 0.1%
2333
 
0.2%
31106
0.8%
42650
1.9%
ValueCountFrequency (%)
263
 
< 0.1%
254
 
< 0.1%
247
 
< 0.1%
2329
< 0.1%
2258
< 0.1%

precipitation9am
Real number (ℝ)

UNIQUE

Distinct142193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.00074753
Minimum-17.73934574
Maximum32.47858981
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-05-07T15:53:25.655549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-17.73934574
5-th percentile1.765127641
Q16.650237922
median10.00000857
Q313.38930622
95-th percentile18.2314101
Maximum32.47858981
Range50.21793555
Interquartile range (IQR)6.739068299

Descriptive statistics

Standard deviation4.997908149
Coefficient of variation (CV)0.4997534569
Kurtosis-0.0003350336479
Mean10.00074753
Median Absolute Deviation (MAD)3.368518937
Skewness-0.005954014114
Sum1422036.293
Variance24.97908586
MonotocityNot monotonic
2021-05-07T15:53:25.854017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.077461451
 
< 0.1%
5.7299154221
 
< 0.1%
1.4827507141
 
< 0.1%
14.059080441
 
< 0.1%
14.196566531
 
< 0.1%
12.470985861
 
< 0.1%
0.90814282911
 
< 0.1%
14.679739391
 
< 0.1%
14.138570231
 
< 0.1%
9.6491190531
 
< 0.1%
Other values (142183)142183
> 99.9%
ValueCountFrequency (%)
-17.739345741
< 0.1%
-11.035403711
< 0.1%
-11.023931311
< 0.1%
-10.698081061
< 0.1%
-10.302856511
< 0.1%
ValueCountFrequency (%)
32.478589811
< 0.1%
32.21008071
< 0.1%
30.355796971
< 0.1%
30.243844881
< 0.1%
30.174273091
< 0.1%

modelo_vigente
Real number (ℝ≥0)

Distinct130381
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2278037905
Minimum0
Maximum0.9994
Zeros455
Zeros (%)0.3%
Memory size1.1 MiB
2021-05-07T15:53:26.099374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004225511698
Q10.0300547619
median0.1008531746
Q30.3295071429
95-th percentile0.8532265297
Maximum0.9994
Range0.9994
Interquartile range (IQR)0.299452381

Descriptive statistics

Standard deviation0.2727641015
Coefficient of variation (CV)1.197364192
Kurtosis0.5595235392
Mean0.2278037905
Median Absolute Deviation (MAD)0.08689381384
Skewness1.340188459
Sum32392.10438
Variance0.07440025509
MonotocityNot monotonic
2021-05-07T15:53:26.305223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0455
 
0.3%
0.00189
 
0.1%
0.000666666666768
 
< 0.1%
0.000565
 
< 0.1%
0.000465
 
< 0.1%
0.00262
 
< 0.1%
0.000222222222258
 
< 0.1%
0.000257
 
< 0.1%
0.000333333333352
 
< 0.1%
0.0002549
 
< 0.1%
Other values (130371)141173
99.3%
ValueCountFrequency (%)
0455
0.3%
6.600660066 × 1063
 
< 0.1%
7.989347537 × 10636
 
< 0.1%
1.075268817 × 1051
 
< 0.1%
1.578947368 × 1051
 
< 0.1%
ValueCountFrequency (%)
0.99941
< 0.1%
0.99926984131
< 0.1%
0.99913095241
< 0.1%
0.99833333331
< 0.1%
0.99807301591
< 0.1%

Interactions

2021-05-07T15:52:00.690008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:00.905260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:01.102862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:01.301602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:01.517026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:01.720759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:52:43.904276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:44.098755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:44.302212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:44.512244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:44.753529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:44.982932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:45.208304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:45.432726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:45.642174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:45.860560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:46.085973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:46.292417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:46.495503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:46.692976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:46.906404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:47.159751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:47.464910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:47.675698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:47.873661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:48.168866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:48.452109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:48.687479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:48.926839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:49.146909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:49.364388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:49.591622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:49.818017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:50.047405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:50.311696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:50.572000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:50.845343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:51.091690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:51.348520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:51.591528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:51.814958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:52.038360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:52.257773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:52.453251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:52.666951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:52.879416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:53.093439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:53.319925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:53.554299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:53.782687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:54.017088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:54.257311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:54.482708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:54.701460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:54.919384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:55.140793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:55.375195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:55.594607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:55.807584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:56.029244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:56.241704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:56.425774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:56.636196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:56.850622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:57.077046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:57.343333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:57.578184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:57.803600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:58.028396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:58.255196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:58.494555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:58.720949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:58.948342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:59.182294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:59.434644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:59.669351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:52:59.889762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:00.336595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:00.554983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:00.754347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:00.968826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:01.180231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:01.381720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:01.611079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:01.848444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:02.136677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:02.381765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:02.629103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:02.866469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:03.131759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:03.352169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:03.598512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:03.877262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:04.104629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:04.318058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:04.535476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:04.741955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:04.944914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:05.166354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:05.376189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:05.575395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:05.805408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:06.021687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:06.241100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:06.456524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:06.673465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:06.887919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:07.097835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:07.320269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:07.522700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:07.756076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:08.007404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:08.237322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:08.474687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:08.715404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:08.903899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:09.130294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:09.354694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:09.573142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:09.804374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:10.014824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:10.230248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:10.438662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:10.645574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:10.864987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:11.087392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:11.299824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:11.513397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:11.752757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:11.972170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:12.175650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:12.390053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:12.593533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:12.776048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:12.984490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:13.194927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:13.410376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:13.639761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:13.854299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:14.084757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:53:14.298891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-07T15:53:26.500699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-07T15:53:27.054189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-07T15:53:27.619704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-07T15:53:28.155246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-07T15:53:28.660209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-07T15:53:14.776713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-07T15:53:16.028572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-07T15:53:17.060714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-07T15:53:18.192081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datelocationmintempmaxtemprainfallevaporationsunshinehumidity9amhumidity3pmpressure9ampressure3pmcloud9amcloud3pmtemp9amtemp3pmraintodayamountOfRainraintomorrowtemphumidityprecipitation3pmprecipitation9ammodelo_vigente
02008-12-01Albury13.422.90.6NaNNaN71.022.01007.71007.18.0NaN16.921.8No0.0No29.4828.400000125.1153600.089825
12008-12-02Albury7.425.10.0NaNNaN44.025.01010.61007.8NaNNaN17.224.3No0.0No32.122.2085691021.4971000.023477
22008-12-03Albury12.925.70.0NaNNaN38.030.01007.61008.7NaN2.021.023.2No0.0No32.8438.0000001720.7828590.027580
32008-12-04Albury9.228.00.0NaNNaN45.016.01017.61012.8NaNNaN18.126.5No1.0No35.6021.200000812.0286460.023962
42008-12-05Albury17.532.31.0NaNNaN82.033.01010.81006.07.08.017.829.7No0.2No40.7641.600000911.8835460.220164
52008-12-06Albury14.629.70.2NaNNaN55.023.01009.21005.4NaNNaN20.628.9No0.0No37.6429.600000817.3209940.056883
62008-12-07Albury14.325.00.0NaNNaN49.019.01009.61008.21.0NaN18.124.6No0.0No32.0024.800000819.7912710.030004
72008-12-08Albury7.726.70.0NaNNaN48.019.01013.41010.1NaNNaN16.325.5No0.0No34.0424.80000077.2070850.023000
82008-12-09Albury9.731.90.0NaNNaN42.09.01008.91003.6NaNNaN18.330.2No1.4Yes40.2812.800000191.5407020.414649
92008-12-10Albury13.130.11.4NaNNaN58.027.01007.01005.7NaNNaN20.128.2Yes0.0No38.1234.40000078.2598330.064945

Last rows

datelocationmintempmaxtemprainfallevaporationsunshinehumidity9amhumidity3pmpressure9ampressure3pmcloud9amcloud3pmtemp9amtemp3pmraintodayamountOfRainraintomorrowtemphumidityprecipitation3pmprecipitation9ammodelo_vigente
1421832017-06-15Uluru2.622.50.0NaNNaN59.024.01025.01021.4NaNNaN8.822.1No0.0No29.0030.8710.5738200.004554
1421842017-06-16Uluru5.224.30.0NaNNaN53.024.01023.81020.0NaNNaN12.323.3No0.0No31.1630.8812.8912780.001849
1421852017-06-17Uluru6.423.40.0NaNNaN53.025.01025.81023.0NaNNaN11.223.1No0.0No30.0832.0723.5092570.010637
1421862017-06-18Uluru8.020.70.0NaNNaN56.032.01028.11024.3NaN7.011.620.0No0.0No26.8440.457.6414670.094063
1421872017-06-19Uluru7.420.60.0NaNNaN63.033.01027.21023.3NaNNaN11.020.3No0.0No26.7241.6168.8630260.016058
1421882017-06-20Uluru3.521.80.0NaNNaN59.027.01024.71021.2NaNNaN9.420.9No0.0No28.1634.4125.8486810.002556
1421892017-06-21Uluru2.823.40.0NaNNaN51.024.01024.61020.3NaNNaN10.122.4No0.0No30.0830.8106.6538790.002053
1421902017-06-22Uluru3.625.30.0NaNNaN56.021.01023.51019.1NaNNaN10.924.5No0.0No32.3627.2919.7159760.023350
1421912017-06-23Uluru5.426.90.0NaNNaN53.024.01021.01016.8NaNNaN12.526.1No0.0No34.2830.8120.9855510.007195
1421922017-06-24Uluru7.827.00.0NaNNaN51.024.01019.41016.53.02.015.126.0No0.0No34.4030.8154.3814810.018811